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The Data Science Workshop

The Data Science Workshop

By : Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare
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The Data Science Workshop

The Data Science Workshop

3 (2)
By: Anthony So , Thomas Joseph, Robert Thas John, Andrew Worsley , Dr. Samuel Asare

Overview of this book

Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities. The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search. Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch. By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently.
Table of Contents (16 chapters)
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Preface
12
12. Feature Engineering

Maximum Depth

In the previous section, we learned how Random Forest builds multiple trees to make predictions. Increasing the number of trees does improve model performance but it usually doesn't help much to decrease the risk of overfitting. Our model in the previous example is still performing much better on the training set (data it has already seen) than on the testing set (unseen data).

So, we are not confident enough yet to say the model will perform well in production. There are different hyperparameters that can help to lower the risk of overfitting for Random Forest and one of them is called max_depth.

This hyperparameter defines the depth of the trees built by Random Forest. Basically, it tells Random Forest model, how many nodes (questions) it can create before making predictions. But how will that help to reduce overfitting, you may ask. Well, let's say you built a single tree and set the max_depth hyperparameter to 50. This would mean that there would...

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